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Purpose <p>To develop and validate a diagnostic model for IgA nephropathy (IgAN) based on Shear Wave Elastography (SWE) and clinical characteristics.</p> Methods <p>This study enrolled 270 patients with primary glomerulonephritis who underwent renal biopsy at the Fifth Affiliated Hospital of Sun Yat-sen University between November 2019 and November 2023. Among the study cohort, there were 152 IgAN patients. Biochemical and ultrasound examinations including SWE examination were performed within one week prior to biopsy. Univariate and multivariate analyses were used to determine the independent predictors of IgAN.</p> Results <p>Multivariate logistic regression identified age, estimated glomerular filtration rate (eGFR), serum albumin, 24-hour urinary protein, and mean SWE value as independent predictors of IgAN. In the training cohort, our model achieved an area under the receiver operating characteristic curve (AUC) of 0.921 (95% CI: 0.879–0.962), and 0.806 (95% CI: 0.707–0.905) in the validation cohort. A nomogram integrating the aforementioned independent risk factors was constructed, facilitating practical use for clinicians.</p> Conclusion <p>The integrated clinical and SWE model demonstrates good diagnostic performance for IgAN. It is simple to implement and holds significant clinical value as a potential non-invasive diagnostic tool.</p>

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Integrating shear wave elastography and clinical characteristics for noninvasive diagnosis of IgA nephropathy

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Jiaxin Chen,
  • Shushang Zhang,
  • Dalin Ye,
  • Shuqing Wang,
  • Qunyan Wu,
  • Yuhong Lin,
  • Zhongzhen Su

摘要

Purpose

To develop and validate a diagnostic model for IgA nephropathy (IgAN) based on Shear Wave Elastography (SWE) and clinical characteristics.

Methods

This study enrolled 270 patients with primary glomerulonephritis who underwent renal biopsy at the Fifth Affiliated Hospital of Sun Yat-sen University between November 2019 and November 2023. Among the study cohort, there were 152 IgAN patients. Biochemical and ultrasound examinations including SWE examination were performed within one week prior to biopsy. Univariate and multivariate analyses were used to determine the independent predictors of IgAN.

Results

Multivariate logistic regression identified age, estimated glomerular filtration rate (eGFR), serum albumin, 24-hour urinary protein, and mean SWE value as independent predictors of IgAN. In the training cohort, our model achieved an area under the receiver operating characteristic curve (AUC) of 0.921 (95% CI: 0.879–0.962), and 0.806 (95% CI: 0.707–0.905) in the validation cohort. A nomogram integrating the aforementioned independent risk factors was constructed, facilitating practical use for clinicians.

Conclusion

The integrated clinical and SWE model demonstrates good diagnostic performance for IgAN. It is simple to implement and holds significant clinical value as a potential non-invasive diagnostic tool.